What tricks do people use to manage the available memory of an interactive R session? I use the functions below [based on postings by Petr Pikal and David Hinds to the r-help list in 2004] to list (and/or sort) the largest objects and to occassionally rm() some of them. But by far the most effective solution was … to run under 64-bit Linux with ample memory.
Any other nice tricks folks want to share? One per post, please.
# improved list of objects
.ls.objects <- function (pos = 1, pattern, order.by,
decreasing=FALSE, head=FALSE, n=5) {
napply <- function(names, fn) sapply(names, function(x)
fn(get(x, pos = pos)))
names <- ls(pos = pos, pattern = pattern)
obj.class <- napply(names, function(x) as.character(class(x))[1])
obj.mode <- napply(names, mode)
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
obj.size <- napply(names, object.size)
obj.dim <- t(napply(names, function(x)
as.numeric(dim(x))[1:2]))
vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
obj.dim[vec, 1] <- napply(names, length)[vec]
out <- data.frame(obj.type, obj.size, obj.dim)
names(out) <- c("Type", "Size", "Rows", "Columns")
if (!missing(order.by))
out <- out[order(out[[order.by]], decreasing=decreasing), ]
if (head)
out <- head(out, n)
out
}
# shorthand
lsos <- function(..., n=10) {
.ls.objects(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)
}
To further illustrate the common strategy of frequent restarts, we can use littler which allows us to run simple expressions directly from the command-line. Here is an example I sometimes use to time different BLAS for a simple crossprod.
Likewise,
loads the Matrix package (via the –packages | -l switch) and runs the examples of the spMatrix function. As r always starts ‘fresh’, this method is also a good test during package development.
Last but not least r also work great for automated batch mode in scripts using the ‘#!/usr/bin/r’ shebang-header. Rscript is an alternative where littler is unavailable (e.g. on Windows).